Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Nadharia ya Thamani Iliyokithiri (EVT)× | Nadharia ya Hisa Zinazotambulika na Muundo wa HAR× | |
|---|---|---|
| Nyanja | Fedha | Fedha |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2001 | 2009 |
| Mwanzilishi≠ | Coles (textbook treatment); McNeil, Frey & Embrechts | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| Aina≠ | Tail / extreme-event model | Time-series regression of realized variance |
| Chanzo asilia≠ | Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598 | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. DOI ↗ |
| Majina mbadala | EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold. | Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction. |
| ScholarGateSeti ya data ↗ |
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